Recently, voice-based digital assistants, such as Apple's SIRI, Amazon's Echo, Google's Google Assistant, and Microsoft's Cortana, have been introduced into the marketplace to handle various tasks such as home appliance controls, web search, calendaring, reminders, etc. To initiate the voice-based assistant, users can press a button or select an icon on a touch screen, or speak a trigger phase (e.g., a predefined wake-up command), and then utter a natural language command describing his/her intent.
State of the art natural language processing techniques rely on natural language processing models that are difficult to implement and update, due to the high computation and personnel cost. In addition, lack of sufficient training samples is another reason that the natural language processing models become obsolete and inaccurate. Thus, it would be beneficial to provide a way to improve the implementation and updating of natural language processing models in the context of home appliance control and other similar applications.